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浙江大学学报（理学版）
浙江大学学报（理学版）  2019, Vol. 46 Issue (3): 345-353    DOI: 10.3785/j.issn.1008-9497.2019.03.013
环境科学     
杭州市2017年冬季一次重灰霾过程中PM2.5水溶性离子特征、来源及成因分析
汪鹏, 熊春, 吴育杰, 李臻, 郭平, 陈雪, 俞绍才, 王志彬, 刘维屏
浙江大学 环境与资源学院，污染环境修复与生态健康教育部重点实验室, 空气污染与健康研究中心，浙江 杭州310058
A heavy haze episode in Hangzhou city in the winter of 2017: Characteristics of PM2.5 water-soluble components，causes and origins
Peng WANG, Chun XIONG, Yujie WU, Zhen LI, Ping GUO, Xue CHEN, Shaocai YU, Zhibin WANG, Weiping LIU
Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, Research Center for Air Pollution and Health, College of Environmental & Resource Sciences, Zhejiang University, Hangzhou 310058
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摘要： 利用在线监测仪测量了杭州市一次重灰霾过程（2017年12月29日至2018年1月3日）中PM2.5主要水溶性离子（Cl-、SO42-、NO3-、NH4+、Na+、Ga2+、Mg2+）及主要气态污染物（SO2、NO2、O3、NO、CO、HCl、NH3、HNO2、HNO3）的小时浓度。结合混合受体模型和国控监测分析，研究了2017年12月30-31日重灰霾事件的污染特征、来源和成因。研究结果表明：PM2.5浓度高达318 μg·m-3； NO3-/SO42-最大值为2.68，说明移动源污染是杭州市PM2.5形成的重要来源； PM2.5/CO最高达到0.19，说明二次细颗粒物对PM2.5贡献很大；NO3-、SO42-、NH4+的浓度总和占PM2.5平均浓度的64.3%，说明二次无机细颗粒物是杭州重灰霾形成的重要原因，且NO3-的贡献最大，占33.5%。混合受体模型分析显示，杭州市重灰霾污染的潜在源区主要位于安徽、江苏、河南、山东四省交界处，以及安徽省中东部、蚌埠、芜湖等工业污染较为严重的城市。夹杂着大量污染物的北方干冷空气远距离传输叠加部分局地源是杭州此次重雾霾形成的根本原因。因此，为了改善杭州市空气质量，不仅需控制当地的污染物排放，而且还需对整个长三角地区甚至跨区域采取大气联防联控策略。
关键词： 重灰霾;  混合受体模型;  PM2.5水溶性离子;  杭州    
Abstract: The hourly concentrations of water soluble ions (Cl－、SO42－、NO3－、NH4+、Na+、Ga2+、Mg2+) of PM2.5 and the major gaseous pollutants (SO2、NO2、NO、CO、HCl、NH3、HNO2、HNO3) floating in the atmosphere in Hangzhou during the period of December 29, 2017 to January 3, 2018 were measured by online monitoring instruments. With the hybrid receptor model and observations data at the national monitoring stations, we analyzed the characteristics, sources and origins of this heavy haze episode in Hangzhou based on the data recorded during December 30 to 31, 2017. It showed that during the heavy haze episode, the maximum values of PM2.5 and NO3－/SO42－ were 318 μg·m－3 and 2.68, respectively, indicating that the mobile source was one of the major contributors to the formation of PM2.5, and the maximum value of PM2.5/CO was 0.19, indicating the significant contribution of secondary PM. The fact that the total concentrations of NO3－, NH4+ and SO42－ account for 64.3% of PM2.5 concentrations with the 33.5% biggest contribution from NO3－ indicated that the formation of haze was mainly caused by the secondary inorganic aerosols in Hangzhou. The results of the hybrid receptor model analyses reveal that potential source areas for the haze in Hangzhou are mainly located in the border areas of the four provinces (Anhui, Jiangsu, Henan and Shandong), central-southern Anhui province, and cities with highly polluted industries such as Bengbu and Wuhu. In addition to the local sources, long-distance transport of a large amount of air pollutants brought by the cold dry air flow from the north is a fundamental cause of this heavy haze episode in Hangzhou. Therefore, to improve the air quality in Hangzhou, it is necessary to control local pollution emissions, and also call for a joint control strategy for air pollution emissions in the Yangtze River Delta, and even bigger regions.
Key words: heavy haze    hybrid receptor model    PM2.5 water-soluble icons    Hangzhou city
收稿日期: 2018-03-12 出版日期: 2019-05-25
CLC:  X 513  
基金资助: 国家自然科学基金资助项目（21577126, 41561144004）； 科技部重点专项（2016YFC0202702）.
通讯作者: ORCID：http://orcid.org/0000-0001-6006-4100， E-mail：shaocaiyu@zju.edu.cn     E-mail: shaocaiyu@zju.edu.cn
作者简介: 汪鹏（1993-），ORCID：http://orcid.org/0000-0002-9747-2922,男,硕士，主要从事大气污染研究.
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引用本文:

汪鹏, 熊春, 吴育杰, 李臻, 郭平, 陈雪, 俞绍才, 王志彬, 刘维屏. 杭州市2017年冬季一次重灰霾过程中PM2.5水溶性离子特征、来源及成因分析[J]. 浙江大学学报（理学版）, 2019, 46(3): 345-353.

Peng WANG, Chun XIONG, Yujie WU, Zhen LI, Ping GUO, Xue CHEN, Shaocai YU, Zhibin WANG, Weiping LIU. A heavy haze episode in Hangzhou city in the winter of 2017: Characteristics of PM2.5 water-soluble components，causes and origins. Journal of ZheJIang University(Science Edition), 2019, 46(3): 345-353.

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https://www.zjujournals.com/sci/CN/10.3785/j.issn.1008-9497.2019.03.013    或    https://www.zjujournals.com/sci/CN/Y2019/V46/I3/345

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